Informatics Publishing Limited

Ankita Jain ¹, Illyas Khan ², Bhupendra Verma ³

Affiliations
1 Technocrats Institute of Technology, Bhopal, India, Masters in Technology in Information Technology, IN
2 Department of IT Truba Institute of Technology, Bhopal, IN
3 Technocrats Institute of Technology, Bhopal, IN

Abstract

The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Web mining is the application of data mining technologies to automatically interact and discover information from web documents, which can be in structured or unstructured form. We present an enterprise framework regarding semantic web mining, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of educational information systems and standards in the educational field. For online education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology.

Keywords

Semantic Web Mining, Ontology, RDF, XML.

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Pragya Tiwari ¹, Illyas Khan ¹

Affiliations
1 Technocrats Institute of Technology, Bhopal, IN

Abstract

Text categorization is a conventional classification problem applied to the textual domain. It solves the problem of assigning text content to predefined categories. Automatic classification schemes can greatly facilitate the process of categorization. Categorization of documents is challenging, as the number of discriminating words can be very large. The traditional method of text categorization like KNN has a defect that the time of similarity computing is huge. In this paper, neural network technique Back propagation Layer and SOM Algorithm is proposed. The objective of this paper is to reduce the time and effort the user has to spend to find the information sought after. Keywords and phrases increase the effectiveness and efficiency of the search process. In the proposed approach, latent semantic indexing of SOM can be used to enhance the association between terms. A brief review is given on existing document clustering techniques. The proposed method will be efficient in terms of computational cost, accuracy and visualization. It can be easily adapted for large data set.

Keywords

KNN, ANN, Back Propagation, SOM.

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